This study investigates the relationship between consumers’ fiscal and inflation expectations using granular survey data. After applying various methods to reduce endogeneity bias and providing several robustness checks, we show that consumers’ fiscal expectations positively affect their inflation expectations. Moreover, we demonstrate that this link is nonlinear and becomes stronger with the deterioration of the fiscal stance, particularly in response to increases in consumer expectations regarding future fiscal expansions. This novel empirical finding is especially relevant from both fiscal and monetary policy perspectives.

Spatial econometric models allow for interactions among cross-sectional units through spatial weight matrices. This paper parameterizes each spatial weight matrix in the widely used spatial Durbin model with a different instead of one common distance decay parameter, using negative exponential and inverse distance matrices. We propose a joint estimation approach of the decay and response parameters, and we investigate its performance in a Monte Carlo simulation experiment. We also present the results of an empirical application on military expenditures. Indirect effects in particular appear to be sensitive to different parameterizations.

This letter deals with a very simple question: if we have grouped data with a binary-dependent variable and want to include fixed effects in the specification, can we meaningfully compare results using a linear model to those estimated with a logit? The reason to doubt such a comparison is that the linear specification appears to keep all observations, whereas the logit drops the groups where the dependent variable is either all zeros or all ones. This letter demonstrates that a linear specification averages the estimates for all the homogeneous outcome groups (which, by definition, all have slope coefficients of zero) with the slope coefficients for the groups with a mix of zeros and ones. The correct comparison of the linear to logit form is to only look at groups with some variation in the dependent variable. Researchers using the linear specification are urged to report results for all groups and for the subset of groups where the dependent variable varies. The interpretation of the difference between these two results depends upon assumptions which cannot be empirically assessed.

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

In order to guarantee the success of the nascent cellulose-based biofuel industry, it is crucial to identify the most economically relevant components of the biofuel production path. To this aim, an original stochastic financial model is developed to estimate the impact that different feedstock production and biofuel conversion parameters have on the probability of economic success. Estimation of the model was carried out using Monte Carlo simulation techniques along with parametric maximum likelihood estimation procedures. Results indicate that operational efficiency strategies should concentrate on improving feedstock yields and extending the feedstock growing season.